{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3d93e4ef-cdca-41ce-8a14-c309293e1b7a",
   "metadata": {},
   "source": [
    "# Note on the Notebook\n",
    "\n",
    "The examples in this notebook are inspired by real-world scenarios I've observed or experienced across different projects. They touch on several of the points discussed during the PoC to Production talk.\n",
    "\n",
    "Each example is designed to:\n",
    "- Work within the limitations of the free Gurobi restricted license, and  \n",
    "- Fit within the time constraints of our session.\n",
    "\n",
    "While the datasets and models here are much smaller than those used in production, the lessons, best practices, and pitfalls to avoid remain exactly the same.\n",
    "\n",
    "In every example that follows, something is wrong.  \n",
    "Your mission, should you choose to accept it, is to identify the culprit(s) using what you've learned so far.\n",
    "\n",
    "**Happy debugging!**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01e4826a-4bd3-459a-8a5f-7bc172826f18",
   "metadata": {},
   "source": [
    "# Install Required Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be1fa81a-da20-4e24-82ac-8c0b91eca877",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install gurobipy\n",
    "%pip install pandas\n",
    "# %pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da02e9c4-cb70-441f-9890-cd6ba0f40a4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages\n",
    "import math\n",
    "import random\n",
    "import pandas as pd\n",
    "import gurobipy as gp\n",
    "from gurobipy import GRB\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c038388",
   "metadata": {},
   "source": [
    "## Set a path to the data used\n",
    "**If you downloaded the notebook/data and are running this locally**, make sure the data is in the same directory as the notebook and run the cell below, or update it to the correct path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a89d263",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell if you're working locally on your machine.\n",
    "# Change the path if needed.\n",
    "path = ''"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f31bd577",
   "metadata": {},
   "source": [
    "**If you are running this in Colab** run this cell below to set the path to read the data from the GitHub repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0fd6095",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell if you are using Colab.\n",
    "path = 'https://raw.githubusercontent.com/Gurobi/modeling-examples/master/optimization301/PoC_to_Production_Exercise/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b1d2eb8-8875-4eec-8fca-001285136186",
   "metadata": {},
   "source": [
    "# Example 1: Assign Items to Resources\n",
    "Replace *items* with orders, goods, products, groceries, food, or workers, and *resources* with trucks, buses, baskets, or boxes, and you'll start seeing this problem everywhere.\n",
    "\n",
    "Any time you have a limited number of resources with finite capacity, and you need to place, match, assign, or pack your items into those resources, often with goals such as minimizing the number of resources used, you're dealing with a variation of the _bin packing problem_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96e1ee44-cd58-4593-bbe9-539cefbefb40",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ex1():\n",
    "    # Load the data\n",
    "    orders = pd.read_csv(path+'ex1_orders.csv')\n",
    "    vehicles = pd.read_csv(path+'ex1_vehicles.csv')\n",
    "    num_orders = len(orders)\n",
    "    num_vehicles = len(vehicles)\n",
    "    # Build the model\n",
    "    model = gp.Model('vehicle_loading')\n",
    "    # x[i,t]: 1 if order i assigned to vehicle t\n",
    "    x = model.addVars(num_orders, num_vehicles, vtype=GRB.BINARY, name='x')\n",
    "    # y[t]: 1 if vehicle t is used\n",
    "    y = model.addVars(num_vehicles, vtype=GRB.BINARY, name='y')\n",
    "\n",
    "    # Each order assigned to exactly one vehicle\n",
    "    model.addConstrs((x.sum(i, '*') == 1 for i in range(num_orders)), name='assign')\n",
    "    # vehicle capacity constraints\n",
    "    model.addConstrs(\n",
    "        (gp.quicksum(orders.loc[i, 'weight'] * x[i, t] for i in range(num_orders))\n",
    "         <= vehicles.loc[t, 'capacity'] * y[t] for t in range(num_vehicles)), name='capacity')\n",
    "\n",
    "    # Objective: minimize number of used vehicles\n",
    "    model.setObjective(y.sum(), GRB.MINIMIZE)\n",
    "    model.Params.OutputFlag = 0  # turn off the log\n",
    "    model.optimize()\n",
    "    if model.status == GRB.OPTIMAL:\n",
    "        print(f'Optimal. Best Objective: {model.ObjVal}')\n",
    "    elif model.status == GRB.INFEASIBLE:\n",
    "        print('Infeasible!')\n",
    "    else:\n",
    "        print(f'Model Status: {model.status}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e52e5c20-a0e7-4271-9f27-884dc7f5db6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Infeasible!\n"
     ]
    }
   ],
   "source": [
    "ex1()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c990e8a-7afc-4281-9981-75e412d84087",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Solution 1: \n",
    "The model looks correct. You can also save it as an `.lp` file using `model.write('vehicle_loading.lp')` and inspect it if needed. Sometimes issues become clearer when you look at the generated model.\n",
    "\n",
    "However, the first step, like any other problem, is to check the data. Doing so reveals that some orders have weights exceeding the vehicle capacity, which makes the model infeasible.\n",
    "\n",
    "A simple safeguard is to validate your inputs before building the model, for example, by checking that no item exceeds the resource capacity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a23751c-a2fc-4c1e-9351-84850327871f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put this in the function, after reading the input data\n",
    "# Validate the data\n",
    "max_capacity = vehicles['capacity'].max()\n",
    "invalid_orders = orders[orders['weight'] > max_capacity]\n",
    "if not invalid_orders.empty:\n",
    "    print(f'Max vehicle capacity is: {max_capacity}.\\nInvalid orders:\\n{invalid_orders}')\n",
    "    print('\\nFix the input data first, before running the model!')\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35533a22-16fe-4c7a-a3db-38259127ef0f",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Example 2: Unexpected Cost\n",
    "You're working on a problem to determine the optimal flow of products between distribution centers (DCs) and customers, given supply and demand, with the goal of minimizing transportation cost.\n",
    "\n",
    "You've been told that the current solution is around \\\\$6M, and the optimal solution of the model is expected not to exceed this value.  \n",
    "However, the model's optimal solution is close to \\\\$8M. What do you think is going wrong?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e2c281e-3c4d-4d73-a8f6-38945522163a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ex2():\n",
    "    dcs_df = pd.read_csv(path+'ex2_dcs.csv')\n",
    "    cust_df = pd.read_csv(path+'ex2_customers.csv')\n",
    "    lanes_df = pd.read_csv(path+'ex2_lanes.csv')\n",
    "    # Built some objects for convenience and speed\n",
    "    dc = dcs_df['dc_id'].tolist()\n",
    "    cust = cust_df['cust_id'].tolist()\n",
    "    arcs = list(lanes_df[['dc', 'cust']].itertuples(index=False, name=None))\n",
    "    supply = dcs_df.set_index('dc_id')['supply'].to_dict()\n",
    "    demand = cust_df.set_index('cust_id')['demand'].to_dict()\n",
    "    cost = lanes_df.set_index(['dc', 'cust'])['cost'].to_dict()\n",
    "\n",
    "    # Model\n",
    "    model = gp.Model('transport')\n",
    "    # x[d,c]: flow from dc d to customer c\n",
    "    x = model.addVars(arcs, lb=0.0, vtype=GRB.CONTINUOUS, name='x')\n",
    "\n",
    "    # supply constraints\n",
    "    model.addConstrs((x.sum(d, '*') <= supply[d] for d in dc), name='supply')\n",
    "    # demand constraints\n",
    "    model.addConstrs((x.sum('*', c) >= demand[c] for c in cust), name='demand')\n",
    "    # Objective: minimize total transport cost\n",
    "    model.setObjective(gp.quicksum(cost[d, c] * x[d, c] for (d, c) in arcs), GRB.MINIMIZE)\n",
    "    model.Params.OutputFlag = 0  # turn off the log\n",
    "    model.optimize()\n",
    "    if model.Status == GRB.OPTIMAL:\n",
    "        print(f'Objective: {model.ObjVal:,.0f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b6e29fe0-fc81-4f1f-8b3a-f7d6791b6eec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Objective: 7,909,174\n"
     ]
    }
   ],
   "source": [
    "ex2()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c66f65f0-ab70-44fd-b9ea-ca1a3a9282a4",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Solution 2:\n",
    "This example also falls into the category of validating input data. However, unlike before, there's nothing obviously wrong with the values themselves.  \n",
    "If you have the habit of doing exploratory data analysis (EDA), you may spot what's wrong much faster.  \n",
    "This dataset contains some outliers that cause the numbers to spike. As you probably know, a good way to detect outliers is to visualize the data.\n",
    "\n",
    "**In fact, visualization, if possible, is always a good rule of thumb, both for inputs and solutions.**\n",
    "\n",
    "In this case, creating a simple map of the geographical locations can be very revealing. You'll notice that three of the customer locations are in Hawaii (HI).  \n",
    "What the stakeholders didn't mention when giving you the \\$6M benchmark is that customers in HI were out of scope!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3feef743-1f31-4fe5-a6fc-d6e75cb1d6a7",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_ex2():\n",
    "    dcs = pd.read_csv(path+'ex2_dcs.csv')\n",
    "    customers = pd.read_csv(path+'ex2_customers.csv')\n",
    "    fig = go.Figure()\n",
    "    # Customers\n",
    "    fig.add_trace(go.Scattermap(\n",
    "        lat=customers[\"lat\"], lon=customers[\"lon\"],\n",
    "        mode=\"markers\",\n",
    "        marker=dict(size=7),\n",
    "        customdata=customers[\"cust_id\"],\n",
    "        hovertemplate=\"Customer %{customdata}<extra></extra>\",\n",
    "        # name=\"Customers\",\n",
    "        showlegend=False))\n",
    "\n",
    "    # DCs\n",
    "    fig.add_trace(go.Scattermap(\n",
    "        lat=dcs[\"lat\"], lon=dcs[\"lon\"],\n",
    "        mode=\"markers+text\",\n",
    "        marker=dict(size=18, symbol=\"triangle-up\"),\n",
    "        text=dcs[\"dc_id\"],\n",
    "        textposition=\"top center\",\n",
    "        textfont=dict(size=14),\n",
    "        hoverinfo=\"skip\",\n",
    "        # name=\"DCs\",\n",
    "        showlegend=False))\n",
    "\n",
    "    fig.update_layout(\n",
    "        map=dict(\n",
    "            style=\"open-street-map\",\n",
    "            zoom=2.2,\n",
    "            center=dict(lat=36, lon=-100)),\n",
    "        margin=dict(l=0, r=0, t=0, b=0),\n",
    "        showlegend=False)\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5658a99a-cb07-4091-9818-6380728fe78d",
   "metadata": {},
   "outputs": [
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          "contourcarpet": [
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ex2()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b226934-98a3-4571-b317-ca0217ea706e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Example 3: Why Infeasible?\n",
    "There are situations where a set of tasks share limited resources, meaning some tasks cannot be executed simultaneously because they depend on or compete for the same resource. We can model this as a compatibility graph, where each task is a node and an edge between two tasks indicates that they have no conflict and can run in parallel. If there is no edge between two tasks (i.e., no compatibility), they cannot both be in the solution. The goal is to find the largest subset of mutually compatible tasks; those that can all be performed at the same time without resource overlap. This is known as the _maximum clique problem_.\n",
    "\n",
    "This problem appears in social network analysis (e.g., finding the largest group of mutual friends), telecom, bioinformatics, manufacturing, and many other domains.\n",
    "\n",
    "In the example below, find out why the function returns infeasible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a4cfe5e-139e-44b9-a823-911ecd6538f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ex3():\n",
    "    # Using this to ensure all are working with the same data\n",
    "    n, p, seed = 200, 0.95, 42\n",
    "    random.seed(seed)\n",
    "    edges = set()\n",
    "    for i in range(n):\n",
    "        for j in range(i + 1, n):\n",
    "            if random.random() < p:\n",
    "                edges.add((i, j))\n",
    "\n",
    "    model = gp.Model('max_clique')\n",
    "    model.Params.TimeLimit = 3\n",
    "    model.Params.OutputFlag = 0\n",
    "\n",
    "    x = model.addVars(n, vtype=GRB.BINARY, name='x')\n",
    "    for i in range(n):\n",
    "        for j in range(i + 1, n):\n",
    "            if (i, j) not in edges:  # no edge, no compatibility\n",
    "                model.addConstr(x[i] + x[j] <= 1, name=f'conflict_{i}_{j}')\n",
    "    # Find the maximum set of compatible nodes\n",
    "    model.setObjective(x.sum(), GRB.MAXIMIZE)\n",
    "    model.optimize()\n",
    "    if model.status == GRB.OPTIMAL:\n",
    "        print(f'Optimal. Best Objective: {model.ObjVal}')\n",
    "    else:\n",
    "        print('Infeasible')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d9f67074-d986-49ab-8a51-167b4de8799f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter TimeLimit to value 3\n",
      "Infeasible\n"
     ]
    }
   ],
   "source": [
    "ex3()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f66547b-e2f0-4e69-85b3-a4c77336df8a",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Solution 3:\n",
    "This model cannot be solved within the 3-second time limit set by the parameter. Because of how the model is written, if the solution is not optimal, it mistakenly returns an \"Infeasible\" message.\n",
    "\n",
    "So, what's a better approach? At the very least, you should handle the model's status more carefully:\n",
    "- If the model is not **Optimal**, it can have several other statuses. At minimum, return the actual status to see what it is.\n",
    "- Check for all relevant statuses that matter in your use case. For example, are you fine with returning the best feasible solution if the time limit is reached?\n",
    "- If the model is genuinely **Infeasible**, do you simply want to return that message, or handle it differently?\n",
    "\n",
    "Bottom line:  \n",
    "**Always check against all the solver statuses that may occur in your model, based on your configuration.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36f41f5d-95cf-4a28-a926-59a581e0a0be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example of a different process for status. Replace the end of ex3() function with this.\n",
    "if model.status == GRB.OPTIMAL:\n",
    "    print(f'Optimal. Best Objective: {model.ObjVal}')\n",
    "elif model.status == GRB.TIME_LIMIT:\n",
    "    # CAUTION: Timelimit may be reached and still no feasible solution is found.\n",
    "    # It's best to check the model for solution values that'll be populated in case of feasibility.\n",
    "    obj_val = model.ObjVal if hasattr(model, 'ObjVal') else 'Not Found'\n",
    "    print(f'Timelimit has reached. The best feasible solution objective: {obj_val}')\n",
    "elif model.status == GRB.INFEASIBLE:\n",
    "    # Better to have a mechanism to deal with this.\n",
    "    # e.g., ComputeIIS or another model that's triggered in these cases\n",
    "    print('Infeasible')\n",
    "else:\n",
    "    print(f'Model Status for investigation: {model.status}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b54ac5-210b-412d-a155-6dc49c87c20e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Example 4: Non-Integer Integers! \n",
    "This small model looks straightforward with two integer variables.\n",
    "But when you look at the optimal values, something seems off. What's going on?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2cdceeb6-f74a-45f9-ba6c-3721d13dd44d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ex4():\n",
    "    model = gp.Model()\n",
    "    x = model.addVar(vtype=GRB.INTEGER, ub=1000, name=\"x\")\n",
    "    y = model.addVar(vtype=GRB.INTEGER, ub=1000, name=\"y\")\n",
    "    # max πx + ey\n",
    "    model.setObjective(math.pi * x + math.e * y, GRB.MAXIMIZE)\n",
    "    model.addConstr(math.pi * x + 1 / 3 * y <= 3141.59265)\n",
    "    model.addConstr(x + y <= 1000)\n",
    "    model.Params.OutputFlag = 0\n",
    "    model.optimize()\n",
    "    if model.Status == GRB.OPTIMAL:\n",
    "        print(f'Optimal Solution: {model.ObjVal}')\n",
    "        for v in model.getVars():\n",
    "            print(v.VarName, v.X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a2bd651-dd0d-48bf-8c80-9976db000db3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal Solution: 3141.5926529489225\n",
      "x 999.9999987232932\n",
      "y 1.2397618199993859e-06\n"
     ]
    }
   ],
   "source": [
    "ex4()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "566308e3-b066-4e87-a4cb-5cb97eb39263",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Solution 4:\n",
    "The \"non-integer\" values in the solution are actually integer-feasible within Gurobi's tolerance. By default, Gurobi's integer feasibility tolerance ([IntFeasTole](https://docs.gurobi.com/projects/optimizer/en/current/reference/parameters.html#intfeastol)) is 1e-5. \n",
    "\n",
    "In this example, both `x` and `y` are within the tolerance and thus count as integer-feasible. \n",
    "If you need stricter integer precision, you can tighten the `IntFeasTol` parameter to a smaller value.\n",
    "\n",
    "When reporting results, you might choose to round integer values for clarity, but be careful.\n",
    "A **common mistake** is using exact equality checks for **binary variables**, such as `v.X == 1` or `v.X == 0`.\n",
    "As seen in this example with integer values, solver tolerances can produce results like 0.999999 or 1.2e-6.\n",
    "Using direct equality tests can misclassify such variables.\n",
    "Instead, use a tolerance-aware check, treat a binary variable as 1 if `v.X > 0.5`.\n",
    "\n",
    "When you enable the Gurobi log output, you may also notice that the solver reports two feasible solutions.\n",
    "You can inspect the alternative feasible solution using Gurobi's solution pool features as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0d4f6e80-9540-410e-9ee5-20ef83dc2539",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ex4_solution():\n",
    "    model = gp.Model()\n",
    "    x = model.addVar(vtype=GRB.INTEGER, ub=1000, name=\"x\")\n",
    "    y = model.addVar(vtype=GRB.INTEGER, ub=1000, name=\"y\")\n",
    "    # max πx + ey\n",
    "    model.setObjective(math.pi * x + math.e * y, GRB.MAXIMIZE)\n",
    "    model.addConstr(math.pi * x + 1 / 3 * y <= 3141.59265)\n",
    "    model.addConstr(x + y <= 1000)\n",
    "    # model.Params.OutputFlag = 0  # Extra: Turn it on. Look at solution count\n",
    "    model.Params.IntFeasTol = 1e-9  # Something to play with. What happens? Why?\n",
    "    model.optimize()\n",
    "    if model.Status == GRB.OPTIMAL:\n",
    "        # This part is extra and unrelated. Just want to show you how to check the solutions of the solution pool.\n",
    "        for k in range(model.SolCount):\n",
    "            model.Params.SolutionNumber = k\n",
    "            print(f'Solution {k}: obj={model.PoolObjVal}')\n",
    "            for v in model.getVars():\n",
    "                print(v.VarName, v.Xn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4775ae8d-efeb-4ccc-ba60-95ceab0e0fda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter IntFeasTol to value 1e-05\n",
      "Gurobi Optimizer version 12.0.3 build v12.0.3rc0 (win64 - Windows 11+.0 (26200.2))\n",
      "\n",
      "CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512]\n",
      "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n",
      "\n",
      "WLS license 945452 - registered to Decision Spot\n",
      "Optimize a model with 2 rows, 2 columns and 4 nonzeros\n",
      "Model fingerprint: 0x55387b9a\n",
      "Variable types: 0 continuous, 2 integer (0 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [3e-01, 3e+00]\n",
      "  Objective range  [3e+00, 3e+00]\n",
      "  Bounds range     [1e+03, 1e+03]\n",
      "  RHS range        [1e+03, 3e+03]\n",
      "Found heuristic solution: objective 3141.1693428\n",
      "Presolve time: 0.00s\n",
      "Presolved: 2 rows, 2 columns, 4 nonzeros\n",
      "Variable types: 0 continuous, 2 integer (0 binary)\n",
      "\n",
      "Root relaxation: objective 3.141593e+03, 2 iterations, 0.00 seconds (0.00 work units)\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "*    0     0               0    3141.5926530 3141.59265  0.00%     -    0s\n",
      "\n",
      "Explored 1 nodes (2 simplex iterations) in 0.02 seconds (0.00 work units)\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 2: 3141.59 3141.17 \n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 3.141592652949e+03, best bound 3.141592652949e+03, gap 0.0000%\n",
      "Solution 0: obj=3141.5926529489225\n",
      "x 999.9999987232932\n",
      "y 1.2397618199993859e-06\n",
      "Solution 1: obj=3141.1693427646624\n",
      "x 999.0\n",
      "y 1.0\n"
     ]
    }
   ],
   "source": [
    "ex4_solution()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ff384d2-173a-40da-b53b-4f98ce70a88a",
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   "source": [
    "# Example 5: Unexpected Solution\n",
    "We're revisiting Example 2, but with a new constraint.  \n",
    "This time, we want to decide which DCs to open and determine the flow of products from the open DCs to customers, given supply and demand, to minimize total cost (transportation + fixed opening costs).\n",
    "\n",
    "But the optimal solution is totally unexpected. See if you can find out why."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "704e66ff-cab2-439e-869a-63fcf76e4c19",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ex5():\n",
    "    dcs_df = pd.read_csv(path+'ex2_dcs.csv')\n",
    "    cust_df = pd.read_csv(path+'ex2_customers.csv')\n",
    "    lanes_df = pd.read_csv(path+'ex2_lanes.csv')\n",
    "\n",
    "    # Built some objects for convenience and speed\n",
    "    dc = dcs_df['dc_id'].tolist()\n",
    "    cust = cust_df['cust_id'].tolist()\n",
    "    arcs = list(lanes_df[['dc', 'cust']].itertuples(index=False, name=None))\n",
    "    supply = dcs_df.set_index('dc_id')['supply'].to_dict()\n",
    "    dem_max = cust_df.set_index('cust_id')['demand'].to_dict()\n",
    "    dem_min = cust_df.set_index('cust_id')['min_demand'].to_dict()\n",
    "    cost = lanes_df.set_index(['dc', 'cust'])['cost'].to_dict()\n",
    "\n",
    "    # Model\n",
    "    model = gp.Model('facility_location')\n",
    "    # x[d,c]: flow from dc d to customer c\n",
    "    x = model.addVars(arcs, lb=0.0, vtype=GRB.CONTINUOUS, name='x')\n",
    "    # y[d]: 1 if dc d is open\n",
    "    y = model.addVars(dc, vtype=GRB.BINARY, name='y')\n",
    "\n",
    "    # per customer: min_demand[c] <= sum_d x[d,c] <= demand[c]\n",
    "    model.addConstrs((x.sum('*', c) >= dem_min[c] for c in cust), name='min_demand')\n",
    "    model.addConstrs((x.sum('*', c) <= dem_max[c] for c in cust), name='max_demand')\n",
    "\n",
    "    # dc capacity: sum_c x[d,c] <= supply[d] * y[d]\n",
    "    model.addConstrs((x.sum(d, '*') <= supply[d] * y[d] for d in dc), name='capacity')\n",
    "\n",
    "    # Objective: shipping + fixed cost of opening a dc\n",
    "    shipping = gp.quicksum(cost[d, c] * x[d, c] for (d, c) in arcs)\n",
    "    fixed = 10_000 * y.sum()\n",
    "    model.setObjective(shipping + fixed, GRB.MINIMIZE)\n",
    "    model.Params.OutputFlag = 0\n",
    "    model.optimize()\n",
    "    if model.Status == GRB.OPTIMAL:\n",
    "        print(f'Objective value: {model.ObjVal:.0f}')\n",
    "        print(f'Facilities opened: {sum(int(round(y[d].X)) for d in dc)}')\n",
    "        print(f'Total shipped: {sum(x[arc].X for arc in arcs):.0f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "197f0778-221e-4083-89bf-0092b7c5c804",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Objective value: 0\n",
      "Facilities opened: 0\n",
      "Total shipped: 0\n"
     ]
    }
   ],
   "source": [
    "ex5()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "107307c9-7ac9-43ab-a15f-52e03875efa0",
   "metadata": {},
   "source": [
    "# Solution 5:\n",
    "The model looks correct, and the data also seems fine at first glance. So, what's the issue?\n",
    "\n",
    "In this case, it's a combination of both the input data and how the model is written:\n",
    "- For each customer, the model enforces that the total demand served is between the minimum and maximum demand.  \n",
    "- Each open DC cannot ship more than its available supply.  \n",
    "- The objective is to minimize total cost.  \n",
    "- However, the minimum demand for all customers is set to 0.\n",
    "\n",
    "Fulfilling a demand of 0 for every customer is really easy. No DC needs to open, and no shipping is required. Problem solved!\n",
    "\n",
    "If you set the minimum demand of even one customer to a positive value, you'll immediately see the model produce a more realistic and meaningful solution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2b977bb-0b59-4b17-bdc2-bdb9bf463215",
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   "source": [
    "# Example 6: Another Unexpected Solution\n",
    "Let's look at a simplified version of the facility location problem. Our goal is to decide how many facilities to open and how much to ship from each one to meet total demand at the lowest possible cost.\n",
    "\n",
    "The model appears correct, and the solution is reported as optimal,  but something isn't right. Can you figure out what's happening?\n",
    "\n",
    "_Hint_: It's not related to `model.Params.Presolve = 0`. We turned presolve off intentionally to make sure the issue shows up. In a small model like this, Gurobi's presolve is smart enough that if it were on, the model would solve without showing the problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "029beb81-b58e-4b51-9226-1e26df53e2d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ex6():\n",
    "    num_facilities = 3\n",
    "    demand = 100\n",
    "    shipping_cost = 1\n",
    "    facility_cost = 5000\n",
    "    M = 1e8\n",
    "\n",
    "    model = gp.Model()\n",
    "    model.Params.Presolve = 0\n",
    "    # Quantity to ship from each facility\n",
    "    x = model.addVars(num_facilities, name=\"ship\")\n",
    "    # Whether or not we open a facility\n",
    "    y = model.addVars(num_facilities, vtype=GRB.BINARY, name=\"open_facility\")\n",
    "    # Satisfy customer demand\n",
    "    model.addConstr(x.sum() == demand)\n",
    "    # Only ship if facility is open\n",
    "    for i in range(num_facilities):\n",
    "        model.addConstr(x[i] <= M * y[i])\n",
    "    # Minimize shipping costs + facility-opening costs\n",
    "    model.setObjective(shipping_cost * x.sum() + facility_cost * y.sum(), GRB.MINIMIZE)\n",
    "    model.optimize()\n",
    "    if model.Status == GRB.OPTIMAL:\n",
    "        for v in model.getVars():\n",
    "            print(v.VarName, v.X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "29e8cb39-4b26-40ff-ac35-c2c09f5e042c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Presolve to value 0\n",
      "Gurobi Optimizer version 12.0.3 build v12.0.3rc0 (win64 - Windows 11+.0 (26200.2))\n",
      "\n",
      "CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512]\n",
      "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n",
      "\n",
      "Non-default parameters:\n",
      "Presolve  0\n",
      "\n",
      "WLS license 945452 - registered to Decision Spot\n",
      "Optimize a model with 4 rows, 6 columns and 9 nonzeros\n",
      "Model fingerprint: 0xc27b7ec5\n",
      "Variable types: 3 continuous, 3 integer (3 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 1e+08]\n",
      "  Objective range  [1e+00, 5e+03]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+02, 1e+02]\n",
      "Variable types: 3 continuous, 3 integer (3 binary)\n",
      "Found heuristic solution: objective 15100.000000\n",
      "\n",
      "Root relaxation: objective 1.000050e+02, 3 iterations, 0.00 seconds (0.00 work units)\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "*    0     0               0     100.0050000  100.00500  0.00%     -    0s\n",
      "\n",
      "Explored 1 nodes (3 simplex iterations) in 0.02 seconds (0.00 work units)\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 2: 100.005 15100 \n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 1.000050000000e+02, best bound 1.000050000000e+02, gap 0.0000%\n",
      "ship[0] 0.0\n",
      "ship[1] 0.0\n",
      "ship[2] 100.0\n",
      "open_facility[0] -0.0\n",
      "open_facility[1] -0.0\n",
      "open_facility[2] 1e-06\n"
     ]
    }
   ],
   "source": [
    "ex6()"
   ]
  },
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   "source": [
    "# Solution 6:\n",
    "The problem is the large M value relative to the solver's integrality tolerance that produces incorrect results.  \n",
    "Here, `x` can take a positive value when `y` is zero, which contradicts the constraint that `x` should only be non-zero when `y=1`. This is an example of a trickle flow.\n",
    "\n",
    "**What to do?**  \n",
    "- **Use the Smallest Sufficient M**: Ensure M is large enough to satisfy constraints but avoid arbitrarily large values.\n",
    "- **Use Indicator Constraints** (of the form $𝑦=0 \\Rightarrow 𝑥=0$).\n",
    "  - Constraints are easier to create. It's closer to how you think about a logical constraint.\n",
    "  - Solver may be able to leverage the implicit knowledge of what a constraint does in the solving process and be able to produce a tighter value of M than you. This means, they are more numerically stable. Although they may come at the expense of additional processing time.\n",
    "- If you need stricter integer precision, you can tighten the `IntFeasTol` parameter to a smaller value or use the [IntegralityFocus](https://docs.gurobi.com/projects/optimizer/en/current/reference/parameters.html#integralityfocus) parameter.\n",
    "\n",
    "These options are explored as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "050ad2c4-acd6-4f59-85ff-be1a9b2073f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ex6_solution():\n",
    "    num_facilities = 3\n",
    "    demand = 100\n",
    "    shipping_cost = 1\n",
    "    facility_cost = 5000\n",
    "    M = 1000  # smaller Big M\n",
    "\n",
    "    model = gp.Model()\n",
    "    model.Params.Presolve = 0\n",
    "    # Quantity to ship from each facility\n",
    "    x = model.addVars(num_facilities, name=\"ship\")\n",
    "    # Whether or not we open a facility\n",
    "    y = model.addVars(num_facilities, vtype=GRB.BINARY, name=\"open_facility\")\n",
    "    # Satisfy customer demand\n",
    "    model.addConstr(x.sum() == demand)\n",
    "    # Only ship if facility is open. Using indicator constraints\n",
    "    for i in range(num_facilities):\n",
    "        model.addConstr(x[i] <= M * y[i])  # use a smaller Big M\n",
    "        # model.addConstr((y[i] == 0) >> (x[i] == 0))  # or indicator\n",
    "        # model.addGenConstrIndicator(y[i], 0, x[i] == 0)  # or indicator, but this way\n",
    "    # Minimize shipping costs + facility-opening costs\n",
    "    model.setObjective(shipping_cost * x.sum() + facility_cost * y.sum(), GRB.MINIMIZE)\n",
    "    # model.Params.IntegralityFocus = 1  # you can play with the same large M value as before, but activating this parameter\n",
    "    model.optimize()\n",
    "    if model.Status == GRB.OPTIMAL:\n",
    "        for v in model.getVars():\n",
    "            print(v.VarName, v.X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5495999b-b30d-4a54-9813-1765f0d0447f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Presolve to value 0\n",
      "Gurobi Optimizer version 12.0.3 build v12.0.3rc0 (win64 - Windows 11+.0 (26200.2))\n",
      "\n",
      "CPU model: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, instruction set [SSE2|AVX|AVX2|AVX512]\n",
      "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n",
      "\n",
      "Non-default parameters:\n",
      "Presolve  0\n",
      "\n",
      "WLS license 945452 - registered to Decision Spot\n",
      "Optimize a model with 4 rows, 6 columns and 9 nonzeros\n",
      "Model fingerprint: 0x62489788\n",
      "Variable types: 3 continuous, 3 integer (3 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 1e+03]\n",
      "  Objective range  [1e+00, 5e+03]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+02, 1e+02]\n",
      "Variable types: 3 continuous, 3 integer (3 binary)\n",
      "Found heuristic solution: objective 15100.000000\n",
      "\n",
      "Root relaxation: objective 6.000000e+02, 3 iterations, 0.00 seconds (0.00 work units)\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0  600.00000    0    1 15100.0000  600.00000  96.0%     -    0s\n",
      "H    0     0                    5100.0000000  600.00000  88.2%     -    0s\n",
      "     0     0  600.00000    0    1 5100.00000  600.00000  88.2%     -    0s\n",
      "\n",
      "Explored 1 nodes (3 simplex iterations) in 0.02 seconds (0.00 work units)\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 2: 5100 15100 \n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 5.100000000000e+03, best bound 5.100000000000e+03, gap 0.0000%\n",
      "ship[0] 0.0\n",
      "ship[1] 0.0\n",
      "ship[2] 100.0\n",
      "open_facility[0] -0.0\n",
      "open_facility[1] -0.0\n",
      "open_facility[2] 1.0\n"
     ]
    }
   ],
   "source": [
    "ex6_solution()"
   ]
  },
  {
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   "id": "b952b7bb-2c8b-4978-a1bd-5e25ec4ce1b0",
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   "source": [
    "# Other Cautionary Tales\n",
    "Here, we share additional examples, cautionary tales, and best practices to keep in your toolkit as you move from simple notebooks toward production-ready code.\n",
    "\n",
    "## Failure After Project Handoff\n",
    "- If you can't have unit tests for your code, at least deliver it with a few small datasets (in text formats like CSV or JSON that are version-controlled) that run successfully with your model. These act as your basic tests.\n",
    "- If someone modifies the model and later reports an issue, you can rerun your small test dataset first to check whether it still passes. This helps you isolate whether the problem lies in the code or in the new data.\n",
    "\n",
    "## Data-Specific Rules\n",
    "- It's rare to build a model that runs only once and on a single dataset. Different datasets may include their own rules or hidden constraints that aren't obvious at first.\n",
    "- Your model might run perfectly on one dataset and fail on another because of these differences.\n",
    "- **Test your model on multiple datasets that capture the diversity of the system you're modeling.**\n"
   ]
  }
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